Automatic Coverage Selection for Surface-Based Visual Localization

Mount, James; Dawes, Les; Milford, Michael

doi:10.1109/lra.2019.2928259

Cited by 8 publications

(6 citation statements)

References 39 publications

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“…Our approach to indoor VPR makes use of the fact that floor patches contain useful features in the form of cracks(wear and tear), designs(tiles), dirt/stains as also established in prior literature [18,30,40,27] but we demonstrate its utility without requiring specialised hardware. The floorbased features act as a unique signature for specific places within an indoor region, identifiable even from opposing viewpoints.…”

Section: A Indoor Visual Place Recognition For Opposite Viewpointsmentioning

confidence: 99%

“…They assumed a known initial location and surface textures are leveraged only for bipartite matching between query and reference images. With a focus on surface-based localization, researchers have also explored robust methods for match verification [28] and coverage selection [27] using groundbased imagery. [18] proposed to use floor patches to perform local region matching in order to develop an infrastructurefree localization system.…”

Section: Saliency Of Floor Featuresmentioning

confidence: 99%

“…In contrast to the aforementioned approaches, our proposed pipeline does not require specialised hardware alternatives, for example, downward-facing cameras [30,27] or additional light sources [18,32]. Furthermore, we exploit floor features for feature matching across forward and backward traverses which has not been tackled explicitly by any of the existing floor-based VPR systems.…”

Section: Saliency Of Floor Featuresmentioning

confidence: 99%

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Early Bird: Loop Closures from Opposing Viewpoints for Perceptually-Aliased Indoor Environments

Tourani¹,

Desai²,

Parihar³

et al. 2020

Preprint

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Significant advances have been made recently in Visual Place Recognition (VPR), feature correspondence and localization due to proliferation of deep-learning-based methods. However, existing approaches tend to address, partially or fully, only one of two key challenges: viewpoint change and perceptual aliasing. In this paper, we present novel research that simultaneously addresses both challenges by combining deep-learnt features with geometric transformations based on reasonable domain assumptions about navigation on a ground-plane, whilst also removing the requirement for specialized hardware setup (e.g. lighting, downwards facing cameras). In particular, our integration of VPR with SLAM by leveraging the robustness of deeplearnt features and our homography-based extreme viewpoint invariance significantly boosts the performance of VPR, feature correspondence and pose graph submodules of the SLAM pipeline. For the first time, we demonstrate a localization system capable of state-of-the-art performance despite perceptual aliasing and extreme 180-degree-rotated viewpoint change in a range of real-world and simulated experiments. Our system is able to achieve early loop closures that prevent significant drifts in SLAM trajectories. We also compare extensively several deep architectures for VPR and descriptor matching. We also show that superior place recognition and descriptor matching across opposite views results in a similar performance gain in back-end pose graph optimization.

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Section: A Indoor Visual Place Recognition For Opposite Viewpointsmentioning

confidence: 99%

Section: Saliency Of Floor Featuresmentioning

confidence: 99%

Section: Saliency Of Floor Featuresmentioning

confidence: 99%

See 1 more Smart Citation

Early Bird: Loop Closures from Opposing Viewpoints for Perceptually-Aliased Indoor Environments

Tourani¹,

Desai²,

Parihar³

et al. 2020

Preprint

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“…An alternative was developed by Mount et al [9]. They proposed a method to automatically determine a suitable trade-off between camera coverage area and localization performance, using only a few aligned test images.…”

Section: Approaches To Parameter Optimizationmentioning

confidence: 99%

Model-Based Parameter Optimization for Ground Texture Based Localization Methods

Schmid,

Simon,

Mester

2021

Preprint

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A promising approach to accurate positioning of robots is ground texture based localization. It is based on the observation that visual features of ground images enable fingerprint-like place recognition. We tackle the issue of efficient parametrization of such methods, deriving a prediction model for localization performance, which requires only a small collection of sample images of an application area. In a first step, we examine whether the model can predict the effects of changing one of the most important parameters of feature-based localization methods: the number of extracted features. We examine two localization methods, and in both cases our evaluation shows that the predictions are sufficiently accurate. Since this model can be used to find suitable values for any parameter, we then present a holistic parameter optimization framework, which finds suitable texture-specific parameter configurations, using only the model to evaluate the considered parameter configurations.

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“…An alternative was developed by Mount et al [2019]. They proposed a method to automatically determine a suitable trade-off between camera coverage area and localization performance, using only a few pairs of aligned test images.…”

Section: Related Workmentioning

confidence: 99%

Ground texture based localization

Schmid

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This dissertation is concerned with the task of map-based self-localization, using images of the ground recorded with a downward-facing camera. In this context, map-based (self-)localization is the task of determining the position and orientation of a query image that is to be localized. The map used for this purpose consists of a set of reference images with known positions and orientations in a common coordinate system. For localization, the considered methods determine correspondences between features of the query image and those of the reference images. In comparison with localization approaches that use images of the surrounding environment, we expect that using images of the ground has the advantage that, unlike the surrounding, the visual appearance of the ground is often long-term stable. Also, by using active lighting of the ground, localization becomes independent of external lighting conditions. This dissertation includes content of several published contributions, which present research on the development and testing of methods for feature-based localization of ground images. Our first contribution examines methods for the extraction of image features that have not been designed to be used on ground images. This survey shows that, with appropriate parametrization, several of these methods are well suited for the task. Based on this insight, we develop and examine methods for various subtasks of map-based localization in the following contributions. We examine global localization, where all reference images have to be considered, as well as local localization, where an approximation of the query image position is already known, which allows for disregarding reference images with a large distance to this position. In our second contribution, we present the first systematic comparison of state-of-the-art methods for ground texture based localization. Furthermore, we present a method, which is characterized by its usage of our novel feature matching technique. This technique is called identity matching, as it matches only those features with identical descriptors, in contrast to the state-of-the-art that also matches features with similar descriptors. We show that our method is well suited for global and local localization, as it has favorable scaling with the number of reference images considered during the localization process. In another contribution, we develop a variant of our localization method that is significantly faster to compute, as it applies a sampling approach to determine the image positions at which local features are extracted, instead of using classical feature detectors. Two further contributions are concerned with global localization. The first one introduces a prediction model for the global localization performance, based on an evaluation of the local localization performance. This allows us to quickly evaluate any considered parameter settings of global localization methods. The second contribution introduces a learning-based method that computes compact descriptors of ground images. This descriptor can be used to retrieve the overlapping reference images of a query image from a large set of reference images with little computational effort. The most recent contribution included in this dissertation presents a new ground image database, which was recorded with a dedicated platform using a downward-facing camera. In addition to the data, we also explain our guidelines for the construction of the platform. In comparison with existing databases, our database contains more images and presents a larger variety of ground textures. Furthermore, this database enables us to perform the first systematic evaluation of how localization performance is affected by the time interval between the point in time at which the reference images are recorded and the point in time at which the query image is recorded. We find out that for outdoor areas all ground texture based localization methods have reliability issues, if the time interval between the recording of the query and reference images is large, and also if there are different weather conditions. These findings point to remaining challenges in ground texture base localization that should be addressed in future work.

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Automatic Coverage Selection for Surface-Based Visual Localization

Cited by 8 publications

References 39 publications

Early Bird: Loop Closures from Opposing Viewpoints for Perceptually-Aliased Indoor Environments

Early Bird: Loop Closures from Opposing Viewpoints for Perceptually-Aliased Indoor Environments

Model-Based Parameter Optimization for Ground Texture Based Localization Methods

Ground texture based localization

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